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Cohabiting family members share microbiota with one another and with their dogs
Human-associated microbial communities vary across individuals: possible contributing factors include (genetic) relatedness, diet, and age. However, our surroundings, including individuals with whom we interact, also likely shape our microbial communities. To quantify this microbial exchange, we surveyed fecal, oral, and skin microbiota from 60 families (spousal units with children, dogs, both, or neither). Household members, particularly couples, shared more of their microbiota than individuals from different households, with stronger effects of co-habitation on skin than oral or fecal microbiota. Dog ownership significantly increased the shared skin microbiota in cohabiting adults, and dog-owning adults shared more 'skin' microbiota with their own dogs than with other dogs. Although the degree to which these shared microbes have a true niche on the human body, vs transient detection after direct contact, is unknown, these results suggest that direct and frequent contact with our cohabitants may significantly shape the composition of our microbial communities.
https://doi.org/10.7554/eLife.00458.001eLife digest
The human body is home to many different microorganisms, with a range of bacteria, fungi and archaea living on the skin, in the intestine and at various other sites in the body. While many of these microorganisms are beneficial to their human hosts, we know very little about most of them. Early research focused primarily on comparing the microorganisms found in healthy individuals with those found in individuals suffering from a particular illness. More recently researchers have become interested in more general issues, such as understanding how these collections of microorganisms, which are also known as the human microbiota or the human microbiome, become established, and exploring the causes of similarities and differences between the microbiota of individuals.
We now know that the communities of microorganisms found in the intestines of genetically related people tend to be more similar than those of people who are not related. Moreover, the communities of microorganisms found in the intestines of non-related adults living in the same household are more similar than those of unrelated adults living in different households. We also know that the range of microorganisms found in the intestine changes dramatically between birth and the age of 3 years. However, these studies have focused on the intestine, and little is known about the effect of relatedness, cohabitation and age on the microbiota at other body sites.
Song et al. compared the microorganisms found on the skin, on the tongue and in the intestines of 159 people—and 36 dogs—in 60 families. They found that co-habitation resulted in the communities of microorganisms being more similar to each other, with those on the skin being the most similar. This was true for all comparisons, including human pairs, dog pairs and human–dog pairs. This suggests that humans probably acquire many of the microorganisms on their skin through direct contact with their surroundings, and that humans tend to share more microbes with individuals, including their pets, with which they are in frequent contact. Song et al. also discovered that, unlike what happens in the intestine, the microbial communities on the skin and tongue of infants and children were relatively similar to those of adults. Overall, these findings suggest that the communities of microorganisms found in the intestine changes with age in a way that differs significantly from those found on the skin and tongue.
Recent studies of the human microbiota have focused on multiple body sites in unrelated adults (Costello et al., 2009; Human Microbiome Project Consortium, 2012) or on a single body site, such as the gut, in single individuals over time (Koenig et al., 2011) or family units, including those with mono- and dizygotic twin pairs (Turnbaugh et al., 2008; Yatsunenko et al., 2012). Genetically related individuals, regardless of whether they cohabitate or not at the time of sampling, tend to share more of their gut (fecal) microbes than unrelated individuals (Caugant et al., 1984; Zoetendal et al., 2001; Stewart et al., 2005; Rajilić-Stojanović et al., 2007; Turnbaugh et al., 2008). However, monozygotic twins are not significantly more similar than dizygotic twins (Turnbaugh et al., 2008), indicating that this effect may be influenced by more than genetic similarity. In a study of US teenage mono- and dizygotic twins and their biological parents, we observed that the composition of the fecal microbiota of teens was more similar to that of their parents than unrelated adults, and as similar to that of their fathers as their mothers (Yatsunenko et al., 2012). Moreover, mothers and fathers shared more similar bacterial communities in their guts compared to unrelated individuals living in other households (Yatsunenko et al., 2012), indicating that a shared environment or lifestyle (e.g., contact with same microbial sources or diet) affects the similarity of the fecal microbiota. Family members may also share intestinal bacteria with their household pets (Caugant et al., 1984). Because we leave microbes from our bodies on the surfaces we touch (Fierer et al., 2010; Flores et al., 2011) and at least a moderate level of microbial exchange is facilitated by direct contact, it is conceivable that our body site-associated microbial communities are shaped in part by our surroundings and those we contact on a daily basis. Whether similar patterns to those mentioned exist within non-gut body sites, whether body sites respond differently to factors such as cohabitation and family structure, and how these patterns change with host age remain unknown.
To test the hypothesis that more microbes are shared between individuals who share a greater number of potential microbial sources, we examined the extent to which microbiota are shared among members of households composed of cohabiting heterosexual adults with and without children (offspring), and with and without dogs. If our hypothesis were supported, then cohabiting family members would have microbiota more similar to each other than to members of different households. Furthermore, cohabiting couples with either children or dogs would share more microbial taxa in one or more of their body habitats than those without either, because such households contain, in addition to a shared environment, additional shared sources of potentially unique microbes with which couples are in close contact.
Results and discussion
Study design
We sampled 159 individuals comprising 17 families with cohabiting children aged 6 months to 18 years, 17 families with one or more dogs but no children, 8 families with both children and dogs, and 18 families with neither children nor dogs. Each family consisted of at least two cohabiting adults (which we define as 'partners' or 'couples') between the ages of 26 and 87 years, and all children included in this study were biologically related to and cohabited with the focal couple. Sampling was performed as described in Costello et al. (2009). For humans, fecal, oral (dorsal tongue), forehead, and right and left palm communities were sampled (n = 5 samples per individual all taken at a single time point). Dogs were sampled similarly (n = 36), except that all four paws were swabbed (n = 7 samples per dog taken at the same time that humans were sampled). The age and gender of humans surveyed in each family plus the number and breed of dogs in families with pets are summarized in Table 1. All samples were initially frozen at −20°C before they were transferred to the laboratory where they were stored at −80°C until they were subjected to DNA extraction, PCR of the variable region 2 (V2) of bacterial 16S rRNA genes and subsequent multiplex sequencing with an Illumina GAIIx instrument (Illumina, Inc., San Diego, CA; n = 969 samples used for the analyses reported, 74,855,127 total reads; average read length, 105 ± 19 nt). The resulting 16S rRNA dataset was analyzed using UniFrac (Lozupone and Knight, 2005), a phylogeny-based measure of the degree of similarity between microbial communities, to assess patterns of similarity within and between families across body sites.
Strong effect of family membership on the human skin microbiota
Our results revealed that family unit had a strong effect on human microbial community composition across all body sites: at each site, family membership explained a large proportion of the variability in bacterial diversity as measured using Faith's phylogenetic diversity (PD) (Faith, 1992), suggesting that family members tend to harbor similar levels of bacterial diversity (Table 2). Composition was also significantly affected by family membership across all body sites such that communities were more similar within families than between them (Table 3 and Figure 1A–D, dogs, if present, are shown together with family members). This pattern was strongest for skin, a body habitat in constant contact with the external environment. An analysis of similarity (ANOSIM) showed that R ranged from 0.21 to 0.62 for humans; the value was higher for dogs with R = 0.71 for forehead and R = 0.83 for paws. On the forehead and palms, all except the father-to-infant within-family distances were significantly smaller than between-family distances (Figure 1A,B and Table 4).
For tongue and feces, we observed effects at the level of family and partner, although generally of smaller magnitude than for skin sites (R < 0.30). The comparison between partners, which is nested within the comparison between family members, is the strongest for all body sites and likely drives similarities by family at these sites. In contrast, a weaker effect, or no effect, was observed for parent–offspring pairs (Figure 1C,D, R < 0.15). This effect seemed to depend primarily on the age of the child. Although parents may share significantly more similar tongue and gut communities with their own children than with other children at older ages (3–18 years), the same is not the case for parents and their infants. These findings agree with previous studies that found the fecal microbiota of teens to be more similar to that of their parents than to unrelated adults (Yatsunenko et al., 2012). However, our results also suggest that effects of cohabitation are insufficiently strong to overcome differences due to age (which are discussed in more detail in the section 'Effect of age on the human microbiota'), particularly between infants and adults, whose microbiota differ substantially (Palmer et al., 2007; Koenig et al., 2011).
We concluded that a shared environment may homogenize skin communities through contact with common surfaces (including each other). Likewise, it may be easier to exchange skin microbes via exposure to home surfaces or indoor air (both of which are typically dominated by skin-associated microbes ; Fierer et al., 2010), than it is to exchange gut or mouth bacteria, potentially because skin surfaces may be less 'selective' environments compared to the gut or mouth environments.
Effect of age on the human microbiota
Our results suggest that observed microbiota developmental dynamics depend on the body site under consideration (Table 2 and Figure 2). Here, we define 'development' as the rate and pattern with which new hosts (i.e., infants and children) acquire adult-like microbiota over time. As noted previously (e.g., Palmer et al., 2007; Koenig et al., 2011; Yatsunenko et al., 2012), the development of the gut microbiota involves profound alterations in diversity and composition that take place over a relatively protracted timeframe (nominally, 0–3 years in age) (Table 2 and Figure 2). Our study enables us to ask further whether similar dynamics are observed in the contemporaneously sampled oral and skin communities of the same individuals. For oral communities, diversity changed substantially with age (Table 2), with a notable increase between the age of 0 and 3 (Figure 2B), while compositional development, though significant, involved more subtle shifts than those observed over the same age range in the gut (Table 5). On the skin, diversity and composition (i.e., here, strictly membership) changed relatively little with age (Table 2 and Figure 2). Interestingly, however, using a distance metric that emphasizes abundance (weighted UniFrac) reveals a strong developmental shift in skin microbiota on the forehead (Figure 2A), a trend driven in part by changes in the relative abundance of dominant taxa rather than the acquisition/loss of unique taxa with age. This result is consistent with earlier studies (Somerville, 1969; Leyden et al., 1975). For example, we see the relative abundance of Propionibacteria significantly increase on the forehead with age (Table 6), which has been shown to be associated with increasing levels of sebum production (Leyden et al., 1975; McGinley et al., 1980). The lack of a significant effect of age with unweighted UniFrac and the small amount of the variance in microbial diversity explained by age suggests developmental dynamics affected more by environmental exposures (ostensibly, to adult skin microbiota) than by age-associated shifts in the selective landscape (e.g., via introduction of solid food, emergence of teeth, etc.).
Taxa shared by cohabiting partners
We next examined which groups of taxa are shared more between cohabiting partners than by adults from different families. Figure 1E shows an example of the specific taxa that are shared within and between adult partners on the right palm. The taxa driving these differences on the palm are lineages commonly reported in surveys of the human skin microbiota such as Propionibacteria (Costello et al., 2009; Grice et al., 2009). Two of these taxa, Prevotella and Veillonella, are primarily associated with the human oral community (Nasidze et al., 2009), suggesting that at least for cohabiting couples, oral-skin transfer may be moderately frequent. Because these taxa are also found in the gut, we tried to determine the level at which the palm communities contained taxa derived from either oral or fecal sources. Using SourceTracker (Knights et al., 2011), we estimated that on average, ∼11% of the palm community is likely from oral sources, as opposed to <2% from fecal sources. Given that oral bacteria can persist on skin for at least 8 hr (Costello et al., 2009), we do not know whether these patterns are due to repeated inoculation from oral-skin contact or a true establishment of oral microbes on skin habitats. However, these results do suggest that close physical contact (such as that between cohabiting couples) can affect the taxonomic composition of the skin and may explain why these communities are more similar.
Effect of cohabitation with dogs on the human skin microbiota
Interestingly, the similarity in the microbiota of cohabiting individuals extends beyond human-to-human relationships to pet-to-pet and even human-to-pet relationships. The patterns of similarity between cohabiting dogs mimic that of cohabiting people, with skin (fur) sites showing the greatest degree of similarity (Figure 1A–D). Moreover, from a microbial perspective, the skin communities of adults are on average more similar to those of their own dog(s) than to other dogs (Figure 3). Thus, we further explored the effect of dogs on the overall bacterial diversity and composition of their cohabiting owners in more detail. A principal components analysis (PCoA) of the human skin communities did not show strong clustering of the sites by dog-owning status in the three main axes, suggesting that dogs do not have a large effect. However, once age was accounted for, we found that dog ownership also affects the skin communities of adults, such that dog owners share more similar communities than expected by chance (Table 5). This effect is not seen when the weighted UniFrac measure is used, suggesting that dog-owners share similar communities mainly due to the addition of rare rather than abundant taxa. Such effects were detected in adults, but not infants or seniors, and may be due to behavioral differences between age groups that were not measured, such as variation in levels of contact with dogs. Alternatively, the presence of strong age affects as indicated earlier combined with small sample sizes of children with and without dogs may have obscured our ability to detect a significant effect of dogs.
Because dogs appear to have the largest effect on the skin communities of their cohabiting adult owners, we then explored the differences in the number of shared phylotypes (OTUs), and the overall bacterial diversity on the skin of adults with dogs as well as adults with children. Adults who have dogs share more bacterial phylotypes with each other than they do with adults who do not have dogs (Figure 4, top right). Having a dog, then, has an effect of similar size on the number of taxa shared in human skin communities as the effect of living together (i.e., two people who have dogs but do not live together share, on average, about as many phylotypes as two people who live together but who do not have a dog). Adults who have a dog and live together share the greatest number of skin phylotypes while adults who neither have a dog nor live together share the least. We tested the effects of gender, pet ownership, and cohabitation of children using a linear mixed effects model, taking into account the variability in diversity due to family membership, age, and technical differences (e.g., sequencing lane). Of these factors, only dog ownership and gender significantly affected diversity (Table 7). Adults who own dogs tended to have a higher diversity of bacteria on their skin (hands and forehead) than those without dogs (Figure 4, top left, p<0.001, Student's t-test with 10,000 Monte Carlo simulations; Table 2). It should also be noted that consistent with previous studies (Fierer et al., 2008), we found that adult females tend to have a higher diversity of bacteria on their hands than adult males (Table 2).
Effects on other body habitat microbiota and of other environmental microbial reservoirs
In contrast to the skin communities, effects of gender or dogs were not detected in the gut or oral communities (Table 7). In fact, none of the tested factors were identified as important in these communities. Curiously, owning other types of indoor pets (i.e., cats [which were not sampled for this study]) did not have a significant effect on the diversity (Table 7), overall similarity (Table 5), or amount of taxa shared between the skin communities of adult partners (p=0.92, Wilcoxon test). Although family size (i.e., having a child or children) did seem to have a significant effect on whether individuals shared more similar skin communities, having a child in our study cohort also did not have a comparable effect to age or dog ownership on community similarity or diversity (Figure 4, bottom, p=0.05 for diversity; Tables 5 and 7). For example, family size only explained a small proportion of the variability in diversity across all body sites (oral: 0%, palms: <1%, fecal: 3%, forehead: 8%) compared to age (oral: 36%, palms: 17%, fecal: 46%, forehead: 15%). Although the mean difference in the number of phylotypes shared between couples paired from different families with and without children was significant, the size of the difference was minor (<2 phylotypes) and is likely due to the large number of observations in the within and between categories (2757 and 3071 respectively), which gave us the power to detect very small effect sizes (d = 0.085 based on a power analysis given these sample sizes).
Mechanistic considerations
One possible explanation for the large effect of dogs in comparison to children and other pets may be that individuals with dogs harbor taxa different from those without dogs, largely due to the presence of dog-derived bacterial taxa on their skin and presumably from frequent direct contact. One of the main taxa driving the pattern of similarity between dog owners is a family of Betaproteobacteria (Methylophilaceae), a group that was also highly abundant (4.6%) in the mouths of the dogs in this study, consistent with a common occurrence of oral–skin transfer between dogs and their owners. Other taxa include several families of Actinobacteria and a family of Acidobacteria commonly associated with soil (Lauber et al., 2009), all of which were present on the paws and forehead of dogs, although in relatively low abundance (<1%). In addition, characterization of the dog oral and 'skin' (fur and paws) microbiota revealed a greater diversity of taxa than described in humans (Table 8). Whereas human skin tends to be dominated by a few taxa at relatively high abundance (namely Propionibacteriaceae, Streptococcaceae and Staphylococcaceae), dog paws and forehead harbor a more even mixture of taxa commonly found in a variety of host-associated environments including mammalian gut (Enterobacteriaceae, Fusobacteriaceae), mouth (Porphyromonadaceae, Veillonellaceae), and skin (Propionibacteriaceae, Staphylococcaceae), as well as free-living environments such as soil and water (e.g., Hyphomicrobiaceae and Sphingomonadaceae) (Tables 6, 8, and 9). This evenness and diversity of taxa found on dog skin may reflect frequent exposure of these sites to many different sources of microbes, or behavioral differences. The dog gut and tongue communities, on the other hand, harbor microbial communities that are somewhat similar in diversity and composition yet distinct from those in the human counterparts (Figure 5). Collectively, these data suggest that our pets not only harbor a diverse microbial community, but also shed a diverse set of microbiota that may in turn influence our own microbial composition.
Given that recent studies in gnotobiotic and other animal models show pervasive effects of the microbiota on metabolism, immunity, and other aspects of our biology, it is intriguing to consider that who we cohabit with, including companion animals, may alter our physiological properties by influencing the consortia of microbial symbionts that we harbor in and on our various body habitats, and in particular, our skin habitats. One example relates to the hygiene hypothesis, which posits that a broad range of microbial exposures helps educate our developing immune systems to tolerate a variety of environmental antigens, thereby reducing risk for atopic disorders such as asthma and food allergies. Recent studies link early exposure to pets to decreased prevalence of allergies, respiratory conditions, and other immune disorders in later stages of development (Havstad et al., 2011) and skin microbes in particular are now receiving more focus as important players in immune regulation (Naik et al., 2012). Given the potential of skin as a collector and integrator of shared environmental bacteria as demonstrated in this study, identifying exactly how such communities can be functionally affected by environmental exposures may help us better understand how they may be deliberately manipulated in order to prevent or treat disease. Epidemiologic studies of the impact of environmental factors on physiological variations and disease predispositions would be enhanced by integrating microbiological surveys, including time series studies during the first years of postnatal life. These efforts would be timely as we seek to understand the impact of Westernization on human biology and to delineate, from an anthropologic perspective, how different cultural traditions and lifestyles relate to our microbial ecology (Benezra et al., 2012).
Materials and methods
DNA extraction and multiplex sequencing
Request a detailed protocolEach sample was processed using methods and procedures described in previous publications (Hamady et al., 2008; Caporaso et al., 2011). DNA was extracted from each swab using the MOBIO PowerSoil DNA isolation kit (MO BIO Laboratories, Inc., Carlsbad, CA) according to manufacturer instructions with modifications. For each sample, the V2 region of bacterial 16S rRNA genes was amplified in triplicate reactions using the primers F27 (5′-AGAGTTTGATCCTGGCTCAG-3′) and R338 (5′-TGCTGCCTCCCGTAGGAG T-3′) barcoded with a unique12-base error-correcting Golay code for multiplexing. PCR reactions contained 13 μl MO BIO PCR water, 10 μl 5 Prime Hot Master Mix, 0.5 μl each of the forward and reverse primers (10 μM final concentration), and 1.0 μl genomic DNA. Reactions were held at 94°C for 3 min to denature the DNA, run for 35 cycles of amplification at 94°C for 45 s, 50°C for 60 s, and then 72°C for 90 s, and completed with a final extension step of 10 min at 72°C. Amplicons were processed using the MO BIO Ultra Clean-htp 96-well PCR clean up kit and quantified using Picogreen dsDNA reagent in 10-mM Tris buffer (pH 8.0). Equal amounts of amplicons from each reaction for a given sample were pooled, followed by gel purification and ethanol precipitation. Multiplex DNA sequencing was performed with a Illumina GAIIx instrument located in the Center for Genome Sciences and Systems Biology at Washington University School of Medicine. The resulting DNA sequences, OTU table, and associated sample metadata have been deposited in the QIIME database (http://www.microbio.me/qiime/) under the study ID 979 (Song_2012_family_study).
Sequence processing and analysis
Request a detailed protocolSequence data were processed with QIIME v1.4.0-dev (Caporaso et al., 2010) as previously described (Yatsunenko et al., 2012). Sequences were demultiplexed and quality filtered using default QIIME parameters, and 16S rRNA operational taxonomic units (OTUs) were picked using a closed reference OTU picking procedure (QIIME script pick_reference_otus_through_otu_table.py). Briefly, sequences were clustered against the Greengenes database (reference collection, 2011 release) (http://greengenes.lbl.gov/) at 97% identity and those failing to match within this threshold were discarded. Taxonomy was assigned to the retained clusters (OTUs) based on the Greengenes reference sequence and the Greengenes tree was used for all downstream phylogenetic community comparisons. For the 1076 samples, the number of sequences per sample ranged from 1 to 300,473, with a mean of 54,475 sequences per sample (total: 58,615,414). Such large variability in the number of sequences per sample is typical for studies employing high-throughput sequencing methods, the causes of which have not yet been systematically tested to our knowledge. To standardize sequence counts across samples, samples with <5000 sequences per sample were removed. Remaining samples were rarefied to 5000 sequences and further filtered by eliminating samples that had a high probability of being mislabeled (e.g., labeled skin, but likely a tongue or fecal sample; detected using the script supervised_learning.py). The remaining 969 samples were used for all downstream analyses. Phylogenetic diversity (PD) was computed and rarefaction analyses were conducted using the QIIME scripts multiple_rarefaction.py, alpha_diversity.py and collate_alpha.py. Because there are no generally accepted methods for 'denoising' Illumina sequence data, alpha diversity estimates such as PD and OTU counts may be overestimated due to sequencing error. However, overestimation should not affect relative differences in diversity. Analyses of community similarity (β-diversity) were performed by calculating pairwise distances using the phylogenetic metric UniFrac (Lozupone and Knight, 2005). The resulting distance matrices were used for principle coordinates analyses (PCoA).
Statistical analyses
Identification of factors of main effect
Request a detailed protocolWe used a permutational multivariate analysis of variance (PERMANOVA) using the PERMANOVA+ add on (Anderson et al. 2008) to Plymouth Routines In Multivariate Ecological Research (PRIMER v6) package (Clarke and Gorley, 2006). The PERMANOVA analysis was based on the unweighted UniFrac dissimilarity matrix, type III partial sums of squares, and 999 random permutations of the residuals under the reduced model to determine whether communities differ significantly between families. We then used a custom script for an analysis similar to an analysis of similarities (ANOSIM) in R version 2.13.2 (R Development Core Team, 2011) to test the hypothesis that within-family communities are more similar than between-family communities. Using the unweighted UniFrac distance matrix, distances were grouped as 'within family' or 'between family' according to criteria appropriate for the comparison (e.g., for partners, the within family group consisted of distances between male and female adult individuals in the same household and the between family group consisted of distances between all combinations of those same adult males and females from different families). Significance levels were calculated by comparing the R statistic against the distribution generated from 10,000 permutations of the randomized dataset.
To determine whether age, gender, pet ownership, and family size (i.e., cohabitation of children) explain differences in community composition, we used a distance-based linear model analysis with 999 permutations using the package DistLM in the PERMANOVA+ add-on to PRIMER. Models were developed using a step-wise process of adding and removing the factors. Model selection was based on Akaike's Information Criterion with a second order bias correction (AICc).
Description of main effects
Request a detailed protocolWe examined the effect of pet ownership, gender, and the cohabitation of children on the bacterial diversity (measured as phylogenetic diversity [PD]) of each body site using a linear mixed effects model including age group, family membership, family size, sequencing lane, and primer plate as random factors. Variability between left and right palms was also controlled for in the palm model. For each body site, we began with the full model including all random and fixed factors, and fixed factors were subsequently removed in a step-wise manner using the function 'drop1' in R. Akaike's Information Criterion (AIC) values and a chi-square test were used to select the best model for each body site. An increase in the AIC value indicates that the removed factor significantly worsened the fit of the model. All modeling was performed using the function 'lmer' in the R-package 'lme4' (Bates et al., 2008). Differences in alpha diversity between groups were subsequently tested using a t-test with Monte Carlo simulations on the dataset rarefied to 5000 sequences (compare_alpha_diversity.py in QIIME v1.5.0-dev).
To describe changes in the microbial community with age, distances were calculated between each participant and all participants within specified age groups (the core groups of adults were considered 30–45 years old and elderly participants ≥65 years old), averaged for each participant, and then plotted against their age using the QIIME script categorized_dist_scatterplot.py. A linear regression model was fitted to the distance plots using R. Analyses of differences in the number of shared phylotypes between groups were performed using the Wilcoxon test in R.
For all body sites, we tested for differences in taxon abundances across the age groups using an analysis of variance (ANOVA) (otu_category_significance.py in QIIME). Infants were considered to be individuals aged 0–12 months, children/adolescents as 1–17 years, adults as 18–59 years and seniors as ≥60 years. Most of the subjects in the child/adolescent category were between the ages of 1 and 6 years, none were between 7 and 11 years, and five participants were between the ages of 12 and 17 years. Due to the low sample size in the latter age range, post-pubescent subjects/teens were not split into a separate category. Exclusion of these five subjects from the analysis did not significantly affect the results. In all appropriate analyses, p values were adjusted for the number of comparisons made using the Bonferroni method.
Data availability
Data from: Cohabiting family members share microbiota with 1 one another and with their dogsID Study ID: 797 (Song_2012_family_study). Available in the QIIME database at http://www.microbio.me/qiime/. Standard used to collect data: The data collected in this study follows the MIMARKS guidelines for reporting 16S survey data.
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Decision letter
Detlef WeigelReviewing Editor; Max Planck Institute for Developmental Biology, Germany
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Thank you for choosing to send your work entitled "Cohabiting family members share microbiota with one another and with their dogs" for consideration at eLife. Your article has been evaluated by a Senior editor, Detlef Weigel, who also acted as Reviewing editor, and two outside reviewers.
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Song and colleagues present a fascinating, albeit descriptive, 16S rDNA-based survey of human co-habitation, spanning 60 families with children, dogs, both, or neither. As expected, they appear to find that individuals living together have increased similarity in their associated microbial communities, with a markedly stronger effect on skin microbiota, compared to oral or gut microbiotae. An important advance over earlier work is that dog owners may "swap" microbes with their pets, again most clearly shown for skin microbiota. There are, however, three major concerns:
1. The first one relates to the statistical methods used. Rather than pairwise comparisons, the authors need to apply a general linear mixed model that includes as many covariates as possible, such as collection dates, family size, geographic origin, urban/rural sites, presence of other pets, sequencing runs, and so on.
2. The second major concern is that although the authors suggest potential reasons for the apparent sharing of microbiota between dogs and their owners (see "Mechanistic considerations"), they do not explicitly test the hypothesis that the transfer of rare taxa drives these trends. For example, the authors might filter out taxa that are more commonly found in the dog microbiota, or found at much lower abundance in humans, and then test if that significantly changes the observed trends. Another approach might be to use weighted metrics instead (e.g., Figure 1), as unweighted data can be greatly influenced by rare but novel taxa.
3. Third, based on the hypothesis that "more microbes are shared between individuals who share a greater number of potential microbial sources", one expects to see an analysis based on the number of human individuals in the household, since other humans are sources, too. This has been relevant for the hygiene hypothesis (more children in a family corresponds with lower incidence of hay fever in later children). Thus, did the number of people in the household explain any of the variance in these analyses? And do later-birth order children host more diverse microbiotae? Also, what can you say about children with dogs versus children without dogs (i.e., not just adults or household members) – are there positive effects of dog ownership?
Some extra analysis is also required for the following points:
A. It is stated that skin microbiota does not change with age as much as stool microbiota, based on a comparison of individuals of different ages. Later on it is stated that skin microbiota tends to reflect environmental microbiota, in agreement with the hypothesis that humans pick up microbes from their dogs. So one interpretation of the absence of a strong age effect is that the skin is more dominated by the environment than the stool microbiota.
B. Are the taxa shared by dog owning adults actually found on dogs? Are the exact same taxa picked up (at the OTU level)? Also, were there effects of the other pets or were the sample sizes simply too small?
C. Finally, Bruce Levin and colleagues asked similar questions based on E. coli strains isolated from household members and their pets in the 1980s – it would be a shame to omit a comparison to these earlier findings. Please take a look at PMID:6376625, for instance.
https://doi.org/10.7554/eLife.00458.017Author response
1) The first one relates to the statistical methods used. Rather than pairwise comparisons, the authors need to apply a general linear mixed model that includes as many covariates as possible, such as collection dates, family size, geographic origin, urban/rural sites, presence of other pets, sequencing runs, and so on.
We thank the reviewers for this important suggestion. We have addressed this point as follows:
We have applied a linear mixed model and we have added the results of this, as well as additional analyses, to our paper. We are happy to report that these analyses further support all of our main conclusions. A brief summary of the results is included below.
Note: Because none of our response variables were expected to follow a particular alternative distribution, such as a binomial or poisson distribution, we used a linear mixed model instead of a generalized linear mixed model. Response variables that were not normally distributed approached normality after transformation and the transformed values were used in the linear models.
A permutational multivariate analysis of variance (PERMANOVA) identified family membership as having a significant effect on community similarity (unweighted UniFrac distance), further supporting our conclusion that communities are more similar between family members than across families.
Age, sex, pet ownership, and cohabitation of children were modeled to explain community similarity using a permutational distance-based linear model approach. This analysis identified age as an important factor for all of the body sites. Once age was controlled for (i.e., the analysis was run on the adult age group separately), dog ownership was the only factor that explained community similarity for the forehead and palms, while none of the factors explained fecal or oral community similarity.
To determine the effect of pet ownership, sex, and child cohabitation on the bacterial diversity of body sites, we used a linear mixed model with family membership, age group, family size, sequencing lane, and primer plate as random factors. Additional factors were not included due to missing data. We expected differences between sequencing lanes and plates because sample collection and processing were performed sequentially, such that blocks of families, as well as samples from our elderly subjects, were run on the same lane and plate, and we now know that family membership and age both affect patterns of community similarity. This analysis supported our conclusion that dog ownership has an effect on the diversity of skin (forehead and palms), such that not having dogs is associated with lower diversity. Consistent with previous studies, sex was also found to have an effect such that females tend to have a higher bacterial diversity on their palms than males.
2) The second major concern is that although the authors suggest potential reasons for the apparent sharing of microbiota between dogs and their owners (see "Mechanistic considerations"), they do not explicitly test the hypothesis that the transfer of rare taxa drives these trends. For example, the authors might filter out taxa that are more commonly found in the dog microbiota, or found at much lower abundance in humans, and then test if that significantly changes the observed trends. Another approach might be to use weighted metrics instead (e.g., Figure 1), as unweighted data can be greatly influenced by rare but novel taxa.
We have added an analysis of the effect of dog ownership on the weighted UniFrac measure of dissimilarity, the results of which are now appended to Table 5. Briefly, while the effect of dogs on the unweighted UniFrac distance between participants is significant, it is not for the weighted measure, which indicates that rare rather than abundant taxa are driving these patterns.
3) Third, based on the hypothesis that "more microbes are shared between individuals who share a greater number of potential microbial sources", one expects to see an analysis based on the number of human individuals in the household, since other humans are sources, too. This has been relevant for the hygiene hypothesis (more children in a family corresponds with lower incidence of hay fever in later children). Thus, did the number of people in the household explain any of the variance in these analyses? And do later-birth order children host more diverse microbiotae? Also, what can you say about children with dogs versus children without dogs (i.e., not just adults or household members) – are there positive effects of dog ownership?
Our linear mixed model showed that family size only explains a very small proportion of the variability in PD across the body sites (Table 2). A distance-based linear model also testing the effect of family size has now been added to the analyses, the results of which are shown in Table 5.
In response to the question of whether older children host more diverse microbiota, a figure showing scatterplots of PD against age has been added (Figure 3).
The PD of children with dogs was compared with that of children without dogs. Because infants and very young children have been shown to have very different communities from older children, children under the age of 3 years were excluded from this analysis. An effect of owning dogs was not detected, but this is likely due to the low sample size. Of the 20 children aged 3–18 years, only three had dogs and two of these three were from the same family. Therefore these results are not included in the manuscript.
Some extra analysis is also required for the following points:
A) It is stated that skin microbiota does not change with age as much as stool microbiota, based on a comparison of individuals of different ages. Later on it is stated that skin microbiota tends to reflect environmental microbiota, in agreement with the hypothesis that humans pick up microbes from their dogs. So one interpretation of the absence of a strong age effect is that the skin is more dominated by the environment than the stool microbiota.
We have added this observation to the new specific section on age effects.
B) Are the taxa shared by dog owning adults actually found on dogs? Are the exact same taxa picked up (at the OTU level)? Also, were there effects of the other pets or were the sample sizes simply too small?
Yes, taxa shared by dog-owners were also found on dogs, as we had already noted. There were several cat owners, but cats were not sampled. Therefore, we could not determine whether there were taxa that were shared among cat-owners. However, cats did not have a significant effect on PD or community similarity among cat-owners. The sample size was small for other pets.
C) Finally, Bruce Levin and colleagues asked similar questions based on E. coli strains isolated from household members and their pets in the 1980s – it would be a shame to omit a comparison to these earlier findings. Please take a look at PMID:6376625, for instance.
We thank the reviewer for pointing out this pioneering work in which fecal E. coli isolates were compared among parents, children, dogs and cats (28 individual hosts) composing five families (Caugant et al. 1984). It found that isolate types were more likely to be shared within than between families. It also describes one family in which two isolate types were common to both human and non-human family members. We now reference these earlier findings.
https://doi.org/10.7554/eLife.00458.018Article and author information
Author details
Howard Hughes Medical Institute
- Rob Knight
Crohn's and Colitis Foundation of America
- Jeffrey I Gordon
National Institutes of Health (HG4872, HG4866)
- Rob Knight
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
We thank S Whitehead for help with participant recruitment and sampling. This work was supported in part by the Crohns and Colitis Foundation of America, the National Institutes of Health, and the Howard Hughes Medical Institute.
Human subjects: Participants were recruited and sampled using methods approved by the Institutional Review Board at the University of Colorado (Protocol#: 0409.13). Informed consent and consent to publish was obtained from all subjects. The Helsinki guidelines were followed.
Animal experimentation: All animals were handled and sampled according to protocol #1102.06, approved by the approved institutional animal care and use committee (IACUC) of the University of Colorado.
Reviewing Editor
- Detlef Weigel, Max Planck Institute for Developmental Biology, Germany
Version history
- Received: December 13, 2012
- Accepted: March 3, 2013
- Version of Record published: April 16, 2013 (version 1)
© 2013, Song et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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The dynamic interplay between guanine-quadruplex (G4) structures and pathogenicity islands (PAIs) represents a captivating area of research with implications for understanding the molecular mechanisms underlying pathogenicity. This study conducted a comprehensive analysis of a large-scale dataset from reported 89 pathogenic strains of bacteria to investigate the potential interactions between G4 structures and PAIs. G4 structures exhibited an uneven and non-random distribution within the PAIs and were consistently conserved within the same pathogenic strains. Additionally, this investigation identified positive correlations between the number and frequency of G4 structures and the GC content across different genomic features, including the genome, promoters, genes, tRNA, and rRNA regions, indicating a potential relationship between G4 structures and the GC-associated regions of the genome. The observed differences in GC content between PAIs and the core genome further highlight the unique nature of PAIs and underlying factors, such as DNA topology. High-confidence G4 structures within regulatory regions of Escherichia coli were identified, modulating the efficiency or specificity of DNA integration events within PAIs. Collectively, these findings pave the way for future research to unravel the intricate molecular mechanisms and functional implications of G4-PAI interactions, thereby advancing our understanding of bacterial pathogenicity and the role of G4 structures in pathogenic diseases.


text:
Vitalic is a man who knows what he wants. He doesn't always know how to get what he wants, but as far as he's concerned, in terms of his music that's no bad thing. He had his first release on DJ Hell's Gigolo Records, a debut album on PIAS and has remixed some of Electronic music's most respected artists, including Daft Punk, Björk and Jean Michel Jarre.
The Frenchman, real name Pascal Arbez, started his music-making career on an MS-20 and an Ensoniq Mirage sampler, spending countless hours cutting his teeth by learning to program his synths to mimic his hero Jarre's unique tones. His first single, La Rock 01, released in 2001, became a summer anthem and four years later his debut album, OK Cowboy, matched its acclaim. Two albums and a whole heap of remixes later and he's currently embarking on an epic tour, incorporating visuals and a live band. We visited Vitalic's studio in Dijon, France, to talk about his approach to music-making, his ever-morphing studio and putting together his live show.
Did you start out as a producer or a DJ?
"I started as a producer; I wasn't interested in DJing at all. When I was a kid I was really into Jean Michel Jarre so it was my dream to make that sort of music. I bought some cheap synthesizers - my first synth was a Roland Alpha Juno-1 - and I had an Ibanez mixer that was really crap. I had a Korg MS-20, too, which was super-expensive, so I had to work during the summer to pay for it. I bought a Novation Bass Station; I really love all the synths from Novation. I have two UltraNovas because I think they have their own character and you can do anything with them. I bought an Ensoniq Mirage sampler and a Novation Drum Station and triggered the whole lot from an Amiga."
Were you just experimenting with that equipment, or did you take a course in production?
"The MS-20 is a great way to learn about synthesis. I learned to make music this way; there were no presets on my synths and I think that helped me to develop my own sound. I had to learn by myself and it was just trial and error."
Were you trying to make music in the style of Jean Michel Jarre?
"No. I was trying to make Daft Punk-style music, like everyone else [laughs]. But I didn't have the right set-up for that. My main goal was to play live, actually. In Dijon, there was a club with great parties and the scene was really alive. I would play there maybe three times a month, with all of my synths - we had a lot of equipment to bring along. I'd play with other musicians and sometimes we didn't even know what we were about to play. It was a bit messy, but it was a lot of fun. I released a record and started to tour around France. Soon after that I released a record on Gigolo [Poney EP] and suddenly I was touring everywhere."
How did the Gigolo release come about?
"I really wanted to sign to Gigolo because it was a very different type of label. I was never that into pure Techno, but I didn't know exactly what I was looking for. Gigolo had some pure Techno, but it also had sexy Disco stuff like Ms Kittin and The Hacker, so I felt it was the right place for me. I worked on some tracks and gave my CD to DJ Hell, then he called me and wanted to sign it. I had the feeling it was the beginning of something; it was the place to be at the time and I was happy with my record. But as much as I liked Gigolo, I moved to PIAS because Gigolo was a bit messy - it just wasn't right for the album."
In what way was it messy?
"The great thing about Gigolo was that DJ Hell was the master - the god of the label, the new David Bowie. The downside of that was that it meant you didn't have your own space. We were the foot soldiers for DJ Hell and you reach a point where you want to be the warrior. Plus, they were signing too many musicians and it was slow and difficult to communicate. I wanted a company that was really going to get behind me and was focused on what I was doing."
So you had a clear vision for the record?
"My vision is usually: I don't know what I want, but I know what I don't want."
How did your studio grow between the EP and the album?
"My studio has always been a bit spread out. I have three different types of live show and I live between Paris and Dijon, so I have lots of different bits of equipment all over the place. But to be honest, I like to limit myself when I'm making an album. When I made the EP I only had an Elka Synthex, a Mirage sampler - even though I don't really consider that an instrument - and an RSF Kobol…"
Wow, they are quite rare…
"Yeah, and I sold it! I really regret that. I still look on eBay but haven't found one since [laughs]. I also had the Novation UltraNova and I made the whole EP with those four things. For the album, I had a DSI Mono Evolver, a Moog Voyager and an Alesis Andromeda. I think it's better to focus on a few machines rather than having a whole load of sounds and presets to choose from."
So you're not a big plug-in user?
"Now more than then, for sure, but I'm lazy and if I can't get the sound I need straightaway, I get frustrated. Having said that, I'm really crazy about the free TAL U-No-62 Juno plug-in. I use it just about everywhere."
What's your song-writing process?
"Most of the time it's a case of making loops, sketches and ideas. I keep it all on my computer and come back to it later. Other times, I just leave the computer and play everything live, which was the case on La Rock 01. It's not sequenced; I didn't have the MIDI converter for the Synthex so I just played it by hand. I also like playing around with the Korg Monotron - I just leave everything recording and play around."
What are your main sources for drums?
"Right now, I really like the Arturia Spark and MOTU BPM. Ableton's built-in samples are pretty good, too. I only use drum machines, I don't sample very much."
Do you record vocalists yourself?
"Most of the time they record themselves and then send me the files. Then I take what they've recorded and chop it up and play around with it."
I wanted to ask you about distortion because it's a big part of your sound. What are your go-to distortion units?
"I used to just distort with the mixing desk - with the Mackie it used to work nicely - but now I use an effect. Ableton's own Saturator plug-in or the one in the UltraNova are both really cool. I just tend to drive them pretty hard and try to get a good tone out of them."
What would you like to buy next for your studio?
"I went to Japan and I bought a MemoryMoog, but the machine is still getting repaired. It worked in theshop when I bought it, but when it arrived in France it stopped… I'd like to get the ARP 2600 - I love the sound of that. I'd love to get the Analogue Solutions Tereshkova, too. I think modular synths are the next step, but I'm not the kind of person who spends hours researching things online. I much prefer to go to a shop, try it and buy it right there. I'm too impatient to wait for things to be delivered!"
Is it the same when you're making music? Do you need things to happen fast?
"Not really. Sometimes I try and do things that I don't have the skills to do, so I have to walk away from the track for a while and come back to it. But once all the elements are made, it all comes together very quickly."
Your album, Rave Age, was mixed in Paris by Stéphane 'Alf' Briat. Why did you choose him?
"He's Electronic Pop sound is really good and I didn't want the record to have an overly compressed, Techno sound. There are some vocals on there, too, and he has a lot of experience of working with that. The vocals were real - they were made with vocoders or distorted. It's easy to make vocals fit when you distort them, but this time I wanted proper songs and I needed a guy who could do that."
How involved do you get in the mix?
"I'm not super-technical so I need someone at the end of the process. When I'm making music, I need there to be errors. I make music like it's live - like I'm playing the instruments - and I don't think too much about the mix; I'm just trying to get my ideas out. A lot of the sounds that trigger my ideas come from effects - how the reverb creates a new rhythm, the delays, the filtering - but it's mostly about the melodies, the sounds and the synth programming. That's what I know about and that's what I enjoy.
"When I look at magazines like Future Music and I see the Logic sessions with two million tracks, I'm speechless; I can't believe it. I have 12 tracks, maybe 16 if I'm adding vocals. You don't need all those tracks. If you listen to early Daft Punk or Caribou, you can hear they aren't using a lot of tracks, it's just an idea and it's done well with a few elements. I'm more into making every part count."
How much of the song is developed at the mixing stage?
"It can happen that you have a new idea during mixing. Often I'll change some things at the last minute because, even though they were right in my studio, when I take them to another, it doesn't sound as good. For the last album [Rave Age] I ended up changing quite a lot of bass drums and even the lead synth lines at the last minute. It's great that you can do that now, though, without too much hassle."
What's your approach when remixing?
"I try to put my own stamp on the track. I do tend to change a lot of things; I did a remix for Björk [Who Is It] and ended up changing the melody line of the vocals just because it wasn't fitting with the idea I'd come up with [laughs]! Luckily, she liked it. I did it in Ableton and it made her vibrato sound really weird, but I just thought of it as an effect in the end. If I try to sound like someone else, I can't do it. There are many people who sound exactly the same - it's crazy how many people sound like Skrillex. In the beginning, I was really trying to sound like other people - to figure out how they got their sound. I don't do it so much now, but I think it's a process every beginner producer goes through: trying to copy someone else.
"Plus, because I'm not as technical, I'd hear people say, 'Daft Punk use a lot of compression', and I'd have a compressor and I wouldn't know what to do with it! I just decided that I wasn't going to get into that kind of thing and instead wanted to focus on the synthesizer as an instrument. It took me a long time to realise that EQ, compression and mixing techniques are just as much a part of the composition process as the music itself. Plus, you've got to go your own way sometimes, too, and trust what you're doing. Your heroes might have a message that you like, but at some point you've got to find your own message."
Who was the last producer you listened to and thought, 'Wow, how did they do that?'
"I think it was Caribou. The production is so simple; everything has its place. It's a mixture of real instruments and electronics, which I like. The same goes for Four Tet. It's weird because I'm really crazy about stuff that's nothing like what I make. I rarely listen to Techno."
Why do you think that is?
"I don't think there are a lot of new things going on in Techno at the minute. There are a lot of kids discovering it now who don't know about its origins. When I hear a recent track, I can tell if it's a pure copy of something else because I was there at the start. Also, it's party music at the end of the day, so you don't need a lot of elements, just a strong kick and bass. Maybe that makes it harder to be innovative in Techno. It's not just a case of everyone having access to powerful music-making tools, it's also a case of everyone having access to music and production techniques. I remember when we were all trying to imitate Daft Punk; we had no idea what they were doing or what they were using. Despite the fact that we were trying to get their sound, along the way, we found our own sound. I'm not saying music was better when all this technical information wasn't available, or that music was more unique, but I think not having so much information led to you finding your own musical path."
What about electronic music in France then? For the past ten years Electro has dominated it.
"I don't go out too often in Paris because I'm always touring, but the Ed Banger sound got a lot ofattention and was really cool. But then it expanded outside of France and everyone got a bit stuck on it until recently. Now it's all about House music, which I hate [laughs]! I like Disco, but I don't understand the TR-909 kick and hat repetition. It feels like it's all the same. Disco seems colder, more synthesized; there are more arpeggiators and vocals. In House it's just one bit, sampled and repeated. I really love what Diplo does, I think he's one of the best producers in the world, but I couldn't make a party with four hours of that sound!"
Vitalic's most recent EP Fade Away is out now. Keep an eye on his website www.vitalic.org for the latest information and live dates.

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While we mourn the loss of the Google Keyword Tool, we need to ask ourselves if the new Keyword Planner is trustworthy or not. Spoiler alert: It is not…
Around one week ago, we lost the Google Keyword Tool. Replacing the Keyword Tool was the new, but far from improved, Keyword Planner "combining the Keyword Tool and Traffic Estimator into one".
At first glance it sounded promising, and I had big hopes after reading articles such as this one. But after trying it, I am disappointed and a little bit scared.
Let's forget about the lost features like "closely related" for a moment. Even then, there is still a lot that doesn't make sense in Keyword Planner. Most importantly, the volume numbers seem odd, especially for low volume keywords, and the estimated search volume simply cannot be trusted.
At first I thought the confusing volume numbers might be a typical Google-phenomenon where small languages are left out in updates (my language is Norwegian). But after discussing with others within the search-field, internationally, we discovered that the new tool is just as bad in other, bigger languages and countries as well. And not only for SEO-use, many AdWords professionals I have talked to are just as disappointed as me, and feel like they cannot trust the tool anymore.
If you want hard facts, take a look at these numbers from Conductor. They report that even though Google claim search volume numbers will increase in the new platform because the numbers include both mobile and desktop searches, 25% of search volumes have stayed the same or gone down.
Want some examples? Well, here you go:
When finding keywords for our own field, SEO, this happens:
In Keyword Planner, the term "søkemotoroptimalisering" (the Norwegian word for search engine optimisation) have got far bigger volumes than the term"SEO". Actually, "SEO" has no volume at all according to the Keyword Planner. Really? No Norwegians search for "SEO", but you find close to 900 searches for "søkemotoroptimalisering" in a month? Really?
When searching for the exact same keywords in Google Trends, this happens:
Interesting…. So we see totally opposite results for the same language, the same country and the exact same keywords…
In Google Global Market Finder, with search volume from the old Keyword Tool, the numbers are more aligned with Trends, and the opposite of Keyword Planner:
The same thing happens when looking at the volume for some other keywords from our field:
Obviously, no-one searches for "adwords" in Norway, but "seo norway" have 40 searches a month? And no-one searches for "analyse" (Norwegianfor analysis)? Really? Google Trends says otherwise, or to be more specific, the exact opposite:
Comparing the same keywords to Bing's volume (taken from the MOZ keyword difficulty tool with Bing data) makes me even more confused. We know that Bing only have a market share on around 3-4% in Norway, but for these keywords the local volumes are larger than in Google.
So, we get 929 Bing searches for "SEO" a month, but no-one searches for it in Google? Really? "Søkemotoroptimalisering" does not make sense either. It's the same number of searches as in Google, with only 3% of the market share…. It simply does not make sense…
I am also confused as to why the global and local search volumes in the Bing example are the same, even for Norwegian words like "webanalyse" and "analyse", but I will leave it at that for now.
I tried all of the above both with and without inserting a destination page in the Keyword Planner, and the results were the same. I also tried it with several other keyword combinations, and they were all just as confusing.
So what do we do now?
Yes, there are a lot of other keyword research tools. Sadly, many of them get their data from the Google or Bing API, which leaves us confused.
We have tools that fetch keywords from competitor's web sites and links. These are good as well but fail to give us the estimated search volume and potential for those keywords.
We also have, as mentioned before, the Bing Keyword Tool. This is great, if you're doing keyword research for a big, non B2B company using one of the big languages. The problem with a small country/language is that Bing's volume for B2B keywords are too low to show any volume at all.
To conduct great keyword analysis without trusting the Google Keyword Planner, you haveto combine a lot of data, that don't always match up, but that will give you a more clear view.
My advice is to combine the following tools to get as close as possible to a good keyword combination for a website:
The key information from the client (what they believe are their most important keywords)
+ Google Keyword Planner
+ Bing Keyword Tool
+ MOZ keyword difficulty tool
+ Kvasir Nøkkelhullet søketrender (Norwegian tool)
+ Google Trends
+ PPC search query reports from the client's account
+ Google Anaytics data
+ Webmaster Tools search query report
+ The number of results for that exact keyword in Google
+ Common sense
+ Maybe some sort of keyword analysis tool (if you want to pay for it, if not, the free ones mentioned above will do)
= A keyword strategy that takes time, but that can be trusted enough to be implemented and tested
So, keyword analysis lives on, but please Google, please give us the old Keyword Tool back, or at least a Keyword Planner that is trustworthy! We know you still have the Keyword Tool data, it is still in there. Thanks!


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EVERETT— With primary election ballots all counted, Snohomish County emerged as the place with the worst turnout of voters in the state.
Snohomish County earned the dubious distinction with a final turnout figure of 25.6 percent, the county's poorest showing for a non-presidential mid-term primary in two decades.
Turnout reached only 21.9 percent in 1990 and 22.7 percent in 1974, according to county election officials.
"It's extremely disappointing," said Snohomish County Auditor Carolyn Weikel, noting it felt like she threw a party and nobody showed up.
"This is our opportunity to have impact on the government that is in place, and so few people take that opportunity to heart and participate," she said. "It's discouraging."
Overall, the state's three most populous counties— King, Pierce and Snohomish— finished in the bottom five, based on the percentage of voter participation.
Pierce tallied the second-lowest with 27.4 percent turnout, and King was fifth at 29.3 percent. In between were Clark (28.6 percent) and Thurston (29 percent) counties.
On the other end of the spectrum, the rural counties of Ferry (54.2 percent), Garfield (54 percent) and Jefferson (50.6 percent) were the only three to break the 50 percent barrier.
Statewide, with all 39 counties combined, turnout fell just shy of 31.2 percent, which didn't bring a smile to the face of Secretary of State Kim Wyman.
"I was certain we would be higher," she said. But, she noted, turnout has been down in primaries across the country. Washington turnout, poor though it might seem, was the second-highest of any state.
Wyman and Weikel attributed voter disinterest mostly to a general lack of electoral sizzle.
"The more I do this, the more I have come to conclude that it comes down to what's on the ballot," Wyman said. "If the voters think the race is important and they can make the difference, they'll be moved to act. For most voters, nothing lit a fire under them" on Aug. 5.
Unlike other states, Washington's ballot did not contain a race for a U.S. Senate seat or major statewide office or controversial initiative. Those are what typically attract lots of campaign spending and drive up voter participation.
That left a smattering of local ballot measures and contentious contests for legislative seats to bring out more than the hard-core, dedicated voters.
A lack of exciting races is only part of the answer, Weikel said.
"It goes deeper. Not only aren't people interested in voting, people aren't interested in running for government jobs," she said.
Inevitably, a low turnout prompts talk of changing the process in some manner.
Wyman is open to considering moving the primary earlier in the year, maybe to June, as in California, where she once lived. But she knows there's opposition from those who think it will make campaigns run longer and from lawmakers who worry it will hurt their ability to raise money.
Weikel would like a conversation on removing partisan races from the primary ballot when there are only two candidates.
"If the primary election is a tool to move the top two (candidates) forward and you only have two, why is there a primary?" she said.
She understands the objections which political parties and lawmakers would raise, but that doesn't mean it shouldn't be discussed.
In the end, Weikel said, it boils down to inspiring civic participation.
"I can provide the tools to take away the barriers to voting," she said. "It is up to (voters) if they want to make a difference."
Primary turnout
Snohomish County 25.6 %
Pierce County 27.4 %
Clark County 28.6 %
Thurston County 29 %
King County 29.3 %
Ferry County 54.2 %
Garfield County 54 %
Jefferson County 50.6 %
Columbia County 49.8 %
Klickitat County 43.9 %
Statewide: 31.2 %
Source: Secretary of State
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PROVIDENCE, R.I. [Brown University] — A well-preserved tomb of an ancient Maya king has been discovered in Guatemala by a team of archaeologists led by Brown University's Stephen Houston. The tomb is packed with carvings, ceramics, textiles, and the bones of six children, who may have been sacrificed at the time of the king's death.
The team uncovered the tomb, which dates from about 350 to 400 A.D., beneath the El Diablo pyramid in the city of El Zotz in May. The news was made public yesterday during a press conference in Guatemala City, hosted by the Ministry of Culture and Sports, which authorized the work.
Before making the actual discovery, Houston said the team thought "something odd" was happening in the deposit they were digging. They knew a small temple had been built in front of a sprawling structure dedicated to the sun god, an emblem of Maya rulership. "When we sunk a pit into the small chamber of the temple, we hit almost immediately a series of 'caches' — blood-red bowls containing human fingers and teeth, all wrapped in some kind of organic substance that left an impression in the plaster. We then dug through layer after layer of flat stones, alternating with mud, which probably is what kept the tomb so intact and airtight."
Then on May 29, 2010, Houston was with a worker who came to a final earthen layer. "I told him to remove it, and then, a flat stone. We'd been using a small stick to probe for cavities. And, on this try, the stick went in, and in, and in. After chipping away at the stone, I saw nothing but a small hole leading into darkness."
They lowered a bare light bulb into the hole, and suddenly Houston saw "an explosion of color in all directions — reds, greens, yellows." It was a royal tomb filled with organics Houston says he'd never seen before: pieces of wood, textiles, thin layers of painted stucco, cord.
"When we opened the tomb, I poked my head in and there was still, to my astonishment, a smell of putrification and a chill that went to my bones," Houston said. "The chamber had been so well sealed, for over 1600 years, that no air and little water had entered."
The tomb itself is about 6 feet high, 12 feet long, and four feet wide. "I can lie down comfortably in it," Houston said, "although I wouldn't want to stay there."
It appears the tomb held an adult male, but the bone analyst, Andrew Scherer, assistant professor of anthropology at Brown, has not yet confirmed the finding. So far, it seems likely that there are six children in the tomb, some with whole bodies and probably two solely with skulls.
And who was this man? Though the findings are still very new, the group believes the tomb is likely from a king they only know about from other hieroglyphic texts — one of Houston's specialties in Maya archaeology. "These items are artistic riches, extraordinarily preserved from a key time in Maya history," said Houston. "From the tomb's position, time, richness, and repeated constructions atop the tomb, we believe this is very likely the founder of a dynasty."
Houston says the tomb shows that the ruler is going into the tomb as a ritual dancer. He has all the attributes of this role, including many small 'bells' of shell with, probably, dog canines as clappers. There is a chance too, that his body, which rested on a raised bier that collapsed to the floor, had an elaborate headdress with small glyphs on them. One of his hands may have held a sacrificial blade."
The stone expert on site, Zachary Hruby, suspects the blade was used for cutting and grinding through bone or some other hard material. Its surface seems to be covered with red organic residue. Though the substance still needs to be tested, "it doesn't take too much imagination to think that this is blood," Houston said.
"We still have a great deal of work to do," Houston said. "Remember, we've only been out of the field for a few weeks and we're still catching our breath after a very difficult, technical excavation. Royal tombs are hugely dense with information and require years of study to understand. No other deposits come close."
Houston, a 2008 MacArthur fellow, is the Dupee Family Professor of Social Science and professor of anthropology at Brown.
Houston's co-director of the site is Edwin Román. He is working with a group of Brown graduate students and researchers, including Thomas Garrison, a former postdoctoral fellow at the Joukowsky Institute and the Department of Anthropology, and graduate students Sarah Newman, Nick Carter, James Doyle, Alex Knodell, and Alex Smith. Scherer, the bone analyst, is working with graduate student Kate Blankenship and undergraduate Morgan Ritter-Armour on the laboratory portion of the analysis.
The work at El Zotz has been supported by funding from the National Science Foundation and the National Endowment for the Humanities.


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Making snowflakes isn't a new craft. I love to try different ways of doing traditional activities. When I was young my mom and I would make snowflakes like this. So I had to do this with my son.
You will just need paper, hole punch, and scissors.
Fold the paper into a triangle.
Give your child the hole punch and the triangles. Just let them punch away.
You will need to make sure that you don't fold the paper too much or you may encounter this…
Cole made random punches on the first snowflake, but as he started the second one I asked if he could use the hole punch to make a hole that was a line. He looked at me funny for a second, then you could see the light bulb go off. He really got into making clusters and lines.
The end result was a window full of beautiful snowflakes. Now I have passed down this activity to the next generation and I guess we have a new tradition in our house.
Kim is a contributing writer for No Time For Flash Cards, a mom to a toddler, a preschooler, and a foster parent, too. She juggles her day by trying out fun activities and crafts with the kids. After all, she is just a big kid herself. See what she has been up to over at Mom Tried It.


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Obama's abuse of power has put him into the driver seat of the American Auto Industry.
"The nine most terrifying words in the English language are: 'I'm from the government and I'm here to help.'"
Every company that has sought assistance from the Federal Government have come to regret it. Banks are frantically repaying the TARP money they were lent. They have learned a very valuable lesson the hard way. Accepting help from the government is in essence selling their souls to a bunch of "megalomaniac, self righteous, hypocritical, lying devils". Funny how this leads me back to Barack Obama. So now the Federal Government is in the "Car Warranty" business thanks to him. I remember liberals crying about George Bush "abusing his power" as President. George Bush committed many screw ups I'll admit. At least he never did anything like what this neo marxist has done within the last 70 days and counting. I'm almost afraid to even imagine what can happen next month leading up to Obama's first 100 days.


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This month on Open Apple, we sit down with Huibert Aalbers, author of Soundsmith. It's hard to overstate what a platform-defining piece of software this was for the Apple IIgs. Few people appreciated what the audio system in this computer was capable of, until Huibert unlocked it for the world to hear. Games and scene demos would use his tool for the entire life of the machine. Other music trackers came along in later years, but Soundsmith was always there. It turns out platform jealousy can be a powerful force indeed.
Meanwhile, we talk oranges, Taiwanese ham, dying young, and cramming IIc parts in your Franklin. We blow the lid off the French pirate sneakernet and complain about kids today and their disrespect for bytes.
After that, Mike finds beta ROMs, Quinn loses her sense of humor, and they both find GS RAM cards everywhere. It's a IIgs themed episode- all the Ensoniqs and FTAs you can stand. If you're an Atari user*, see if you can spot the backhanded compliment.
You won't want to miss Huibert's amazing project involving IIgs Epluché!
More information on everything discussed in this episode, after the jump.
Continue reading "Open Apple #58 (April 2016) : Huibert Aalbers, IIGS Stuff, Taiwanese Ham"